Practitioners often estimate the Sharpe ratio using annualized monthly data. This paper demonstrates how the bias and precision of the Sharpe improves with monthly versus annual data. I provide small-sample and large-sample formulae for the distribution, highlighting the distinction between the annual and annualized monthly estimators. With more than two years of monthly data the large-sample distributions generally provide a good approximation, simplifying the calculation of confidence intervals; this applies for both normal and non-normal returns. Although these results apply to iid returns they are of practical use, since independence for monthly returns is a good description for many financial assets.
Description
How Precision of the Sharpe Ratio Improves With Monthly Data by Thomas Coleman :: SSRN
%0 Journal Article
%1 coleman2018precision
%A Coleman, Thomas
%D 2018
%I SSRN
%J SSRN eLibrary
%K quantfinance sharpe
%R 10.2139/ssrn.2959632
%T How Precision of the Sharpe Ratio Improves With Monthly Data
%X Practitioners often estimate the Sharpe ratio using annualized monthly data. This paper demonstrates how the bias and precision of the Sharpe improves with monthly versus annual data. I provide small-sample and large-sample formulae for the distribution, highlighting the distinction between the annual and annualized monthly estimators. With more than two years of monthly data the large-sample distributions generally provide a good approximation, simplifying the calculation of confidence intervals; this applies for both normal and non-normal returns. Although these results apply to iid returns they are of practical use, since independence for monthly returns is a good description for many financial assets.
@article{coleman2018precision,
abstract = {Practitioners often estimate the Sharpe ratio using annualized monthly data. This paper demonstrates how the bias and precision of the Sharpe improves with monthly versus annual data. I provide small-sample and large-sample formulae for the distribution, highlighting the distinction between the annual and annualized monthly estimators. With more than two years of monthly data the large-sample distributions generally provide a good approximation, simplifying the calculation of confidence intervals; this applies for both normal and non-normal returns. Although these results apply to iid returns they are of practical use, since independence for monthly returns is a good description for many financial assets.},
added-at = {2018-06-12T06:50:05.000+0200},
author = {Coleman, Thomas},
biburl = {https://www.bibsonomy.org/bibtex/2706e3f6bd714a576b755b4cd78908abe/shabbychef},
description = {How Precision of the Sharpe Ratio Improves With Monthly Data by Thomas Coleman :: SSRN},
doi = {10.2139/ssrn.2959632},
interhash = {ea15772b35afb14380bfe897605b5057},
intrahash = {706e3f6bd714a576b755b4cd78908abe},
journal = {SSRN eLibrary},
keywords = {quantfinance sharpe},
language = {English},
location = {https://ssrn.com/paper=2959632},
publisher = {SSRN},
timestamp = {2018-06-12T06:50:27.000+0200},
title = {How Precision of the {S}harpe Ratio Improves With Monthly Data},
type = {Working Paper Series},
year = 2018
}